论文标题
lib-sibgmu-推荐系统的大学图书馆流通数据集
Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
论文作者
论文摘要
我们根据CC的4.0许可证lib -sibgmu（一个大学图书馆流通数据集）为广泛的研究社区开放，以及该数据集中推荐系统的基准主要算法。对于由矢量化器组成的推荐体系结构，将借入的书籍的历史转变为矢量和基于社区的推荐人，分别训练了培训，我们表明，将FastText模型用作矢量器可提供竞争成果。
We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trained separately, we show that using the fastText model as a vectorizer delivers competitive results.
